Dola Seed 2.0 Pro vs GLM-5.1
Which one, when — curated verdict, not a benchmark table
Dola-Seed-2.0-pro is the cheaper and broader of the two: $0.5 per million input versus $1.4 (2.8x) and $3 output versus $4.4, with a 256000-token context and image and video input alongside text, so it fits multimodal work and high-volume text jobs where cache reads at $0.1 per million matter. glm-5.1 is text-only on a 200000-token window but carries an explicit long-context capability flag, making it the pick when you want that behaviour declared for long document pipelines. Both are current-generation, expose chat, code, reasoning and tools, cap output at 131072 tokens, and let you switch thinking off.
Pricing
| Dola Seed 2.0 Pro | GLM-5.1 | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.5 | $1.4 | 0.36× |
| Output / 1M tokens | $3 | $4.4 | 0.68× |
| Cache read / 1M tokens | $0.1 | $0.26 | 0.38× |
Rates from the live catalog at build time; each model page carries the current card.
Where they sit — input price per 1M tokens across all 63 chat models on this billing unit (log scale)
Capabilities
| Dola Seed 2.0 Pro | GLM-5.1 | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | configurable | configurable |
| Structured output | — | yes |
| Prompt caching | implicit + explicit | implicit (automatic) |
| Cache lifetime | not published | not published |
| Minimum cached prefix | 1024 tokens | not published |
Specs
| Dola Seed 2.0 Pro | GLM-5.1 | |
|---|---|---|
| Input modalities | text image video | text |
| Output modalities | text | text |
| Released | 2026-02-14 | 2026-04-07 |
| Context window | 256K | 200K |
| Max output | 131K | 131K |
| Thinking parameter |
| thinking.type |
| Accepted values | thinking.type
reasoning_effort
| thinking.type
|
| Default | enabled, with reasoning_effort medium and thinking summary on | enabled, and the model automatically determines whether to think |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: Dola Seed 2.0 Pro · GLM-5.1
One prompt, both models — measured through the gateway
Dola Seed 2.0 Pro passed · 3 sentences
Reasoning-enabled LLMs do not generate final answers in one pass: they run hundreds of unshown internal trial, cross-check, and correction steps for every user request, and every one of these hidden steps consumes billable GPU time just like the visible output text. Unlike our prior standard chat workload which only processed exactly what users typed, reasoning mode automatically executes these validation loops by default, with no visible indicator to end users that extra compute is being consumed. This 2x increase is not a billing error or rogue usage spike: industry benchmarks consistently put reasoning workloads at 1.7-2.6x the compute cost of equivalent standard LLM requests, and this pricing dynamic is almost never clearly disclosed on vendor rate cards.
out 563 tok (+416 thinking) latency 9.4 s
GLM-5.1 passed · 3 sentences
Enabling reasoning requires the LLM to generate thousands of invisible "thinking" tokens to work through complex logic before producing the final answer. Because we are billed on total token volume, these intermediate steps are charged at the premium output rate, drastically increasing the variable cost per query. The invoice doubled simply because the model is now doing roughly twice the computational work per request, trading higher variable compute costs for significantly improved analytical accuracy.
out 913 tok (+827 thinking) latency 10.9 s
Instruction following (exactly three sentences — countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
Dola Seed 2.0 Pro passed · 8/8 cases
### Corrected Implementation ```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 return best ``` --- ### Bug Explanation The original code has three critical linked flaws: 1. It initialized run counters `best` and `cur` to `0` instead of `1`, failing to account for the fact that *every individual element is already a valid run of length 1* -- this causes single-element inputs to incorrectly return `0`. 2. `cur` only counted how many extra matching elements followed the start of a run, so it always tracked one less than the actual run length, and it reset to `0` (instead of `1`) when a new run began, preserving this off-by-one error for all runs. 3. […]
out 1278 tok (+951 thinking) latency 16.7 s
GLM-5.1 passed · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 return best ``` The original function tracks the number of adjacent equal pairs rather than the actual run length. By initializing `cur` and `best` to 0 and incrementing `cur` only when a match occurs, a run of length L produces a count of L−1. This causes single‑element inputs to return 0 (since the loop never runs) and generally returns a value one less than the true run length, effectively omitting the final element of any run—including those that end at the last index. […]
out 2642 tok (+2414 thinking) latency 26.1 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
Dola Seed 2.0 Pro passed · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
out 506 tok (+452 thinking) latency 8.6 s
GLM-5.1 passed · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": null } ```
out 1465 tok (+1410 thinking) latency 16.1 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
Dola Seed 2.0 Pro passed · 123 words, 0 banned, 1 question
Today we’re rolling out cross-provider prompt caching for our API gateway. For engineering teams routing LLM requests across OpenAI, Anthropic, Mistral and open source models, this feature stores identical prompt payloads at the gateway layer, rather than relying on per-provider cache implementations limited to single endpoints. How much time and compute could your team save by avoiding redundant token processing for repeated system prompts, context windows, or common user queries? Cache hits return responses in under 10ms, with configurable TTL, granular purge controls, and per-application cache partitioning. Early access teams running support bots, batch inference and internal assistants recorded 42-67% lower LLM spend. This feature is live for all gateway users today, with no required code changes to existing routing workflows. (120 words)
out 1041 tok (+872 thinking) latency 11.4 s
GLM-5.1 passed · 120 words, 0 banned, 1 question
We are thrilled to introduce prompt caching across providers in the API gateway. Why pay twice for the same prompt context? Now, when your application sends identical prompt prefixes to different LLM providers, our gateway automatically caches the input, reducing latency and cutting costs. This feature intelligently recognizes repeated prompt structures across OpenAI, Anthropic, and others, storing them efficiently at the network edge. Developers no longer need to manage separate caching logic for each individual provider. Instead, our unified system handles it directly, ensuring faster response times on all subsequent requests. Stop wasting valuable tokens on redundant processing workloads. Upgrade your integration today and experience immediate performance gains while keeping your infrastructure simple and your overall monthly billing incredibly low.
out 7589 tok (+7447 thinking) latency 188.9 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Switch between them with one line
Both ids are in every tab below — the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="Dola-Seed-2.0-pro",
# model="glm-5.1", # uncomment this line, comment the one above
messages=[{"role": "user", "content": "Summarize this diff"}],
reasoning_effort="medium",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.chat.completions.create({
model: "Dola-Seed-2.0-pro",
// model: "glm-5.1", // uncomment this line, comment the one above
messages: [{ role: "user", content: "Summarize this diff" }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);curl https://synthorai.io/v1/chat/completions \
-H "Authorization: Bearer sk-syn-..." \
-H "Content-Type: application/json" \
-d '{
"model": "Dola-Seed-2.0-pro",
# "model": "glm-5.1", # uncomment this line, comment the one above
"messages": [{"role": "user", "content": "Hello"}],
"reasoning_effort": "medium"
}'package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
Model: "Dola-Seed-2.0-pro",
// Model: "glm-5.1", // uncomment this line, comment the one above
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
ReasoningEffort: openai.ReasoningEffortMedium,
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("Dola-Seed-2.0-pro")
// .model("glm-5.1") // uncomment this line, comment the one above
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));FAQ
Which is cheaper, Dola Seed 2.0 Pro or GLM-5.1?
Dola Seed 2.0 Pro is cheaper on input / 1m tokens ($0.5 vs $1.4, 2.8× apart). Other rows may point the other way — the table above carries the full card, and real cost depends on your mix.
Can I A/B test Dola Seed 2.0 Pro against GLM-5.1 without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key — switching is a one-line model-string change, so you can route a fraction of traffic to each and compare bills directly.
Do Dola Seed 2.0 Pro and GLM-5.1 support prompt caching?
Yes — both bill cached reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.